Development and Testing of a Regional GSI-Based EnKF-Hybrid System for the Rapid Refresh Configuration Yujie Pan 1, Kefeng Zhu 1, Ming Xue 1,2, Xuguang.

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Development and Testing of a Regional GSI-Based EnKF-Hybrid System for the Rapid Refresh

Configuration

Yujie Pan1, Kefeng Zhu1, Ming Xue1,2, Xuguang Wang1,2, Jeffrey S. Whitaker3, Stanley G. Benjamin3 and Stephen S. Weygandt3 and Ming Hu3

Center for Analysis and Prediction of Storms1 and School of Meteorology2

University of Oklahoma, Norman Oklahoma 73072

NOAA Earth System Research Laboratory3, Boulder, Colorado

5th EnKF Workshop

Albany, New York

May 2012

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Outline

Part 1: Introduction to the regional GSI-based EnKF-hybrid data assimilation system

Part 2: Single observation tests

Part 3: Comparison of hybrid with GSI and pure EnKF

EnKF-Hybrid 1 way interactive EnKF-Hybrid 1 way with multi-physics EnKF EnKF-Hybrid 2 way interactive Verification of precipitation forecasts on 13 km grid

''1''1

2'1

1'11

211'1

2

1

2

1

2

1

,

HxyHxyαCαxBx

αx

oToTT

oe JJJJ

R

K

k

ekk

1

'1

' xαxx

3

B 3DVAR static covariance; R observation error covariance; K ensemble size; C correlation matrix for ensemble covariance localization; e

kx kth ensemble perturbation; '1x 3DVAR increment; 'x total (hybrid) increment; 'oy innovation vector;

H linearized observation operator; 1 weighting coefficient for static covariance;

2 weighting coefficient for ensemble covariance; α extended control variable.

Extended control variable method (Lorenc 2003) in 3D GSI hybrid (Wang 2010, MWR):

Extra term associated with extended control variable

Extra increment associated with ensemble

GSI-Hybrid: Method

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EnKF

EnKF—RR

RUC

EnKF Domain207x207 grid points~40 km, 51 levelsPrecip. Forecast Domain532x532 grid points~13 km, 51 levelsPrecip. Verification DomainRUC Domain as indicated

Ensemble members 40 

Experiment Domains

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Observations assimilated

Sounding and profiler Surface data from land stations and ships

Aircraft Satellite retrieve winds

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Single Observation Tests (Comparing GSI, Hybrid and EnKF)

3DVAR

Different weight for the static covariance in Hybrid

Solid line: Height at 600 hPa (background)Shading: Temperature increment

EnKF

Weight=1 Weight=0Weight=0.5

Hybrid

Half staticHalf flow-dependent

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Hybrid GSI-EnKF DA system: 1 way coupling

control forecast Hybrid

control analysis

control forecast

data assimilationFirst guess forecast

EnKF analysis k

member 1 forecast

member 2 forecast

member k forecast

EnKF

EnKF analysis 2

EnKF analysis 1

member 1 forecast

member 2 forecast

member k forecastEnsemble

covariance

……

……

……

Wrf-DFL0 20m 40m

Wrf-DFL0 20m 40m

Wrf-DFL0 20m 40m

GSI

observations

Innovation

EnK

FH

ybri

d

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Experiments Horizontal localization

(KM)

Vertical localization

(ln(p))

Fix inflation Adaptive inflation

EnKF 1000 KM (height

dependent)

1.1/1.6 (height

dependent)

0.1 0.9

Hybrid 1way 1000 KM 1.1

……Time (UTC)3hr fcst00 03

obs obs Obs

12 3hr fcst3hr fcst

BackgroundFields

EnKF & hybrid

Analysis Fields

EnKF & hybrid EnKF & hybrid

2010-05-08 00:00

obs

21 3hr fcst

EnKF & hybrid

2010-05-17 21:00

…………

Interpolation

13 KM 12 hr Fcst

Interpolation

13 KM 12 hr Fcst

Hybrid And EnKF Configuration

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Surface Variables Verification (RMSE; 3-18 hr Forecasts)

Hybrid 1way

EnKF

GSI 3dvar

18h3h

3-18 hour forecasts verification against surface data.

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Verifications Against Soundings (RMSE)

Hybrid 1way

EnKF

GSI-3dvar

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Verifications Against Soundings (RMSE)

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group Long wave Short wave Surface layer PBL Cumulus

p1 rrtm scheme (1) Goddard short wave (2)

Monin-Obukhov (Janjic) scheme (2)

Mellor-Yamada-Janjic TKE scheme (2)

Grell 3D ensemble

scheme (5)

p2 rrtm scheme (1) Dudhia scheme (1) Monin-Obukhov scheme (1)

YSU scheme (1) Kain-Fritsch (new Eta)

scheme (1)

p3 rrtm scheme (1) Goddard short wave (2)

MYNN surface layer (5)

MYNN 2.5 level TKE scheme (5)

Grell-Devenyi ensemble

scheme (3)

p4 GFDL (Eta) longwave (99)

GFDL (Eta) short wave (99)

Monin-Obukhov (Janjic) scheme (2)

Mellor-Yamada-Janjic TKE scheme (2)

Grell 3D ensemble

scheme (5)

p5 rrtm scheme (1) Goddard short wave (2)

Monin-Obukhov (Janjic) scheme (2)

Mellor-Yamada-Janjic TKE scheme (2)

Grell-Devenyi ensemble

scheme (3)

Multi-physics GSI-EnKF Hybrid System Configuration

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Surface variables verification (RMSE; 3-18 hr Forecasts)

When Multiple-physics schemes were employed for EnKF, hybrid was also improved .

Multi-hybrid

Single-hybrid

GSI 3dvar

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Verifications Against Soundings (RMSE)

Multi-hybrid

Single-hybrid

GSI 3dvar

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Sensitivity Tests To Covariance Weight

Experiment Weight to static covariance

Hybrid 00 0.0

Hybrid 01 0.1

Hybrid05 0.5

Hybrid 09 0.9

GSI 3dvar 1.0

Hybrid main parameters:Horizontal localization : ~1100 KMVertical localization : 1.1 ( ln(p) )

Verifications Against Soundings

1100 KM horizontal localization improve the performance of hybrid at jet level

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Hybrid GSI-EnKF DA system: 2 way coupling

member 1 forecast

member 2 forecast

member k forecast

control forecast GSI-ECV

EnKF

control analysis

EnKF analysis k

EnKF analysis 2

EnKF analysis 1

member 1 analysis

member 2 analysis

member k analysis

member 1 forecast

member 2 forecast

data assimilation

control forecast

Ensemble covariance

Re-center E

nSR

analysis ensemble

to control analysis

…… ……

……

……

First guess forecast

GSI

observations

Innovation

member k forecast

Wrf-DFL0 20m 40m

Wrf-DFL0 20m 40m

Wrf-DFL0 20m 40m

Wrf-DFL0 20m 40m

Wang et al. 2011

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Surface Variables Verification (RMSE)

Hybrid 2way

EnKF

GSI-3dvarSingle-physics EnKF was used.

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Verifications Against Soundings (RMSE)

Hybrid 2way

EnKF

GSI-3dvar

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Verifications Against Soundings (RMSE)

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OBS (NCEP Stage IV) GSI EnKF

2010051111

2010051305

11 hr forecast started from 2010051100

5 hr forecast started from 2010051300

Hybrid2way

Hourly Precipitation Forecasts on 13 km Grid

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Verification of Hourly QPF on 13 km Grid

Hybrid 2way

EnKF

GSI

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Conclusions• The GSI-based hybrid (run at 40 km grid spacing for RAP data set and

model), with either 1-way or 2-way interaction with a single-physics EnKF and using equal weight for static and flow-dependent covariances, outperforms the GSI and pure EnKF for most verified variables (relative humidity, temperature, wind), except surface temperature. The advantage lasts up to the 18 hour forecast time.

• The hybrid with half static covariance is better than the one without static covariance, indicating the benefit of including static covariance for the current application.

• EnKF and hybrid predict more accurate precipitation pattern and location on a 13 km grid than GSI, which is also demonstrated by ETS score. But hybrid doesn’t improve the precipitation forecasts as much as EnKF.

• The performance of the EnKF system is noticeably improved when multiple physics schemes are used in the ensemble forecast, especially for temperature and moisture fields.

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Future Plan (in collaboration with GSD and EMC)

• Use height-dependent localization for flow-dependent covariance in the hybrid – found helpful within EnKF

• Use well tuned multi-physics EnKF within 2-way hybrid.

• Test the impact of the strong constraint available in GSI

• Add satellite data.

• Implement and test dual-resolution (40/13 km) hybrid

• Test the system with hourly cycles

• Eventual quasi-operational testing of hourly cycled, two-way interactive EnKF/hybrid system for RAP including radar data.

• Long term: Hybrid system applied to NARRE (North America Rapid Refresh Ensemble) and HRRRE (High-Resolution Rapid Refresh Ensemble)

• Nesting CAPS’s Storm-Scale EnKF within (see Youngsun Jung’s talk)

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Thank you!!

state-dependent covariance inflation

• Fix inflation

• Adaptive inflation

• Final inflation

' '1

1 (ln( ) / )

a a

a

sfc

x x

pb taper lncut

p

' '2

2 2

2 21

a a

f a

a

x x

c

925ap hPa

700ap hPa

500ap hPa

300ap hPa

1 2

1000 , 6sfcp hPa lncut

tape

r(r)

Pre

ssur

e (h

Pa)

Vertical smoothing-scale (vz) in GSI

Step1: vz*( log( p(k-1)/psf )-log( p(k+1)/psf) )/2

Step2: vz=vz/1.5

Vertical smoothing scales in GSI

p(k): average pressure at the k-th model levelpsf: average surface pressure

Convert to vertical grid units

loc = loc*coefficent

0 0.2 0.4 0.6 0.8 1

100

200

300

400

500

600

700

800

900

1000

vert

Pres

sure

(hP

a)

WindRH & T

1 1.1 1.2 1.3 1.4 1.5

100

200

300

400

500

600

700

800

900

1000

hori

Pres

sure

(hP

a)

loc = loc*coefficent